@InProceedings{gombar-EtAl:2017:BSNLP,
  author    = {Gombar, Paula  and  Medi\'{c}, Zoran  and  Alagi\'{c}, Domagoj  and  \v{S}najder, Jan},
  title     = {Debunking Sentiment Lexicons: A Case of Domain-Specific Sentiment Classification for Croatian},
  booktitle = {Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {54--59},
  abstract  = {Sentiment lexicons are widely used as an intuitive and inexpensive way of
	tackling sentiment classification, often within a simple lexicon word-counting
	approach or as part of a  supervised model.  However, it is an open question
	whether these approaches can compete with supervised models that use only
	word-representation features.  We address this question in the context of
	domain-specific sentiment classification for Croatian. We experiment with the
	graph-based acquisition of sentiment lexicons, analyze their quality, and
	investigate how effectively they can be used in sentiment classification.  Our
	results indicate that, even with as few as 500 labeled instances, a supervised
	model substantially outperforms a word-counting model. We also observe that
	adding lexicon-based features does not significantly improve supervised
	sentiment classification.},
  url       = {http://www.aclweb.org/anthology/W17-1409}
}